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A Regularized Graph Neural Network Based on Approximate Fractional Order Gradients

Author

Listed:
  • Zijian Liu

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Yaning Wang

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Yang Luo

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

  • Chunbo Luo

    (School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China)

Abstract

Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP. To effectively learn from graph signals, we propose a regularized graph neural network based on approximate fractional order gradients (FGNN). The regularized graph neural network propagates the information between neighboring nodes. The approximation strategy for calculating fractional order derivatives avoids falling into fractional order extrema and overcomes the high computational complexity of fractional order derivatives. We further prove that such an approximation is feasible and FGNN is unbiased towards the global optimization solution. Extensive experiments on citation and community networks show that the proposed FGNN has improved recognition accuracy and convergence speed than vanilla FGNN. The five datasets of different sizes and domains confirm the great scalability of our proposed method.

Suggested Citation

  • Zijian Liu & Yaning Wang & Yang Luo & Chunbo Luo, 2022. "A Regularized Graph Neural Network Based on Approximate Fractional Order Gradients," Mathematics, MDPI, vol. 10(8), pages 1-20, April.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1320-:d:794850
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    References listed on IDEAS

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    1. Chen, Yuquan & Gao, Qing & Wei, Yiheng & Wang, Yong, 2017. "Study on fractional order gradient methods," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 310-321.
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